In our application, the dataset is very large and in high dimensions, in which case
computing the eigenvalues of the data matrix is a key challenge. Recent
techniques enable the computation of large matrix decompositions in a robust
manner without explicitly forming the entire data matrix in memory [1]}, [2]}.
The key main idea behind these methods is stochasticity.
